Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop a Python module that utilizes Claude 3.5 Haiku and the Guidance library to process retrieved document chunks. The module should generate a JSON object structured as specified in the `StructuredPolicySynthesis` task, extracting arguments for and against, evidence strength, and identifying key controversial points from the text.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
AI-Powered Public Health Consensus: Hepatitis B Vaccine Policy Analysis with Claude 3.5 Haiku
The recent contentious debate surrounding the Hepatitis B vaccine for newborns highlights the complexity of public health policy, where scientific evidence, public sentiment, and expert opinions often diverge. This challenge requires developing an AI system to systematically analyze diverse information sources related to vaccine recommendations, synthesize conflicting viewpoints, and generate structured insights. The system will leverage advanced NLP and LLMs to process scientific papers, public statements, and expert commentary. The goal is to identify key arguments for and against current recommendations, assess the strength of evidence, and provide a nuanced summary that could aid public health officials in making informed decisions. This involves more than just summarization; it requires contextual understanding and structured output generation.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.